Update app.py

app.py

@@ -1,22 +1,16 @@
 from PIL import Image
 import gradio as gr
-import numpy as np
-import random, os, gc, base64, io
-import cv2
+import random, os, gc
 import torch
 from accelerate import Accelerator
-from transformers import pipeline, AutoModel, CLIPTextModel, CLIPTokenizer
+from transformers import pipeline
 from diffusers.utils import load_image
-from diffusers import EulerDiscreteScheduler, UNet2DConditionModel, AutoencoderKL, DiffusionPipeline
-from gradio_client import Client
+from diffusers import EulerDiscreteScheduler, DiffusionPipeline
 
 accelerator = Accelerator(cpu=True)
 pipe = accelerator.prepare(DiffusionPipeline.from_pretrained("stabilityai/sd-turbo", torch_dtype=torch.float32, use_safetensors=True, safety_checker=None))
 pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
-##pipe.text_encoder = CLIPTextModel._from_config(pipe.text_encoder.config)
-##pipe.tokenizer = CLIPTokenizer.from_config(pipe.tokenizer.config)
-##pipe.UNet2DConditionModel = UNet2DConditionModel.from_config("stabilityai/sd-turbo", subfolder="unet")
-##pipe.AutoencoderKL = AutoencoderKL.from_config("stabilityai/sd-turbo", subfolder="vae")
+pipe.unet.to(memory_format=torch.channels_last)
 pipe = accelerator.prepare(pipe.to("cpu"))
 generator = torch.Generator("cpu").manual_seed(random.randint(1, 867346))
 apol=[]
@@ -24,10 +18,11 @@ apol=[]
 def plex(prompt):
     gc.collect()
     apol=[]
-    imags = pipe(prompt=prompt,negative_prompt="bad quality",num_inference_steps=5,width=512,height=512,generator=generator).images[0]
-    apol.append(imags)
+    imags = pipe(prompt=[prompt]*2,negative_prompt=["bad quality"]*2,num_inference_steps=5,width=512,height=512,generator=generator)
+    for i, igs in enumerate(imas["images"]):
+        apol.append(igs)
     return apol
 
-iface = gr.Interface(fn=plex,inputs=gr.Textbox(), outputs=gr.Gallery(columns=2), title="Img2Img_SkyV22CntrlNet_CPU", description="Running on CPU, very slow!")
+iface = gr.Interface(fn=plex,inputs=gr.Textbox(), outputs=gr.Gallery(columns=2), title="Stabilityai SD-Turbo CPU", description="Running on CPU, very slow!")
 iface.queue(max_size=1)
 iface.launch(max_threads=1)